Initial commit: Lush vs Bash AI benchmarking framework

Benchmark harness that uses LLM agents to solve shell scripting tasks
in both Bash and Lush, then compares correctness and code quality.

- CLI with run, run-all, list-tasks, report, and export commands
- Agent loop with retry support via Anthropic Claude provider
- Test harness executing solutions in sandboxed subprocesses
- LLM-driven questionnaire for subjective code quality evaluation
- HTML report export with charts (matplotlib)
- 8 Category A tasks (write-from-scratch in both languages)
- 4 Category B tasks (verify provided Bash, convert to Lush)
- Lush language reference for agent context

tasks/category_b/log_parser.toml

@@ -0,0 +1,33 @@
+name = "log_parser"
+category = "b"
+description = """
+Read log lines from stdin. Each line has the format: "LEVEL: message"
+where LEVEL is one of ERROR, WARN, INFO.
+Count occurrences of each level and print a summary sorted by level name.
+Format: "LEVEL: count"
+"""
+
+bash_source = """
+#!/bin/bash
+while IFS= read -r line || [[ -n "$line" ]]; do
+    echo "${line%%:*}"
+done | sort | uniq -c | while read -r count level; do
+    echo "$level: $count"
+done
+"""
+
+[[test_cases]]
+stdin = """ERROR: disk full
+INFO: started
+WARN: low memory
+ERROR: timeout
+INFO: completed"""
+expected_stdout = """ERROR: 2
+INFO: 2
+WARN: 1"""
+
+[[test_cases]]
+stdin = """INFO: boot
+INFO: ready
+INFO: shutdown"""
+expected_stdout = "INFO: 3"